Generally, the adjustment of physical properties of polyolefin in a commercial plant is largely dependent on experiences of a skilled operator. However, because of the differences in modes of operations of respective operators, the operations are sometimes inconsistent. The modes of operations which are different from operator to operator affect the physical properties of a product (i.e., uniformity) and in the end, even if the products are of the same grade, there still may be differences depending on the lots. Obviously, this is a potential cause of claims or complaints from the end users.
In order to overcome these problems, it is useful to apply the technology, which allows estimation of properties of a product, at the point of its production.
Conventionally, in estimating the properties of a polymer product, various empirical and statistical models have been mainly used, such as empirical correlation, neural network, or partial least square (PLS) models.
While the correlation models of prior art can be relatively easily applied to the steady states wherein the process conditions are stably maintained, the conventional technology is problematic in that it frequently shows large differences from the actual analysis values in the unsteady states or in a grade change, where the process conditions are in the state of flux.
These differences are due to the existence of a process delay. The process delays are expressed in different forms, depending on the intrinsic residence time distribution of various devices. For example, there is a simple case of a push back to a certain period of time (e.g., in pipes), or there may be a push back of extended effects in the form an exponential function, such as in continuous stirred tank reactors (CSTR). For other reaction devices, there may be other peculiar types of residence time distribution.
In particular, in cases of multi-stage continuous polymer plants, up until to the point that the product would be affected by the extended effects caused by a change in operational conditions of the previous stage, there would be a long time delay, depending on the intrinsic residence time distribution characteristics of the process. Consequently, because of a rather simplistic way of estimating the properties of a polymer product from the process variables of the current states, it would be of course accompanied by a significant degree of error.
Meanwhile, a typical method of estimating the properties of polymers by incorporating the residence time distribution of the process is by means of a physical model. It is a method of establishing balance equations for a substance by way of reaction system and then solving said equations. For this method based on said physical model, it is necessary to obtain reaction rate constants and various types of physiochemical constants. However, there lies the problem since it is not so easy to obtain these types of constants. To solve this problem of physical model, the method widely used in the industry is a method of computing cumulative properties (hereinafter cumulative properties model) by calculating the instantaneous properties of a product, followed by the application of residence time distribution and the mixing rule of polymer properties.
The method according to said cumulative properties model has many advantages, such as easy application to the actual process in some cases, and potential utilization of empirical correlation, neural network, PLS models, etc. in estimation of instantaneous properties. However, with respect to the method of cumulative properties model, there is a problem of requiring “model training,” or the data of the steady states for the purposes of carrying out the process of empirical correlation, neural network, or PLS for estimating the properties of a product. In particular, in case of a system with recycle streams which are not maintained constantly by time, it is almost impossible to apply the cumulative properties model.